Scenario planning and risk management

ABSTRACT

Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.:H98230-13-D-0054/0006 awarded by Department of Defense. The Governmenthas certain rights in this invention.

BACKGROUND

The subject disclosure relates to scenario planning, and morespecifically, to artificial intelligence based solutions to scenarioplanning and artificial intelligence based solutions to risk management.

Scenario planning can be used by businesses and governments to developlong term future plans. Scenario planning for risk management puts addedemphasis on identifying the extreme but possible risks that are notusually considered in daily operations. Generally, scenario planning hasrelied upon historical trends, with these historical trends beinganalyzed to determine whether current situations or a related factualpattern matches these historical trends. Subsequent to this analysis,the historical outcome is analyzed to determine a likelihood that acurrent situation may or may not be imminent. Thus, these analyses relyon known or recorded issues, trends, and previous outcomes. Accordingly,new trends are not directly considered outside of known or existingtrends, which can result in inadequate or unlikely projections.

Furthermore, existing systems for emerging risk management are alsoreliant on historical analysis. For example, Smoot et al., U.S. PatentApplication Publication Number 2013/0041712A1, describes an “emergingrisk identification process and tool.” In Smoot et al., it is presentedthat [m]anaging risks may include receiving multiple issues associatedwith an enterprise where each issue is a future risk or a current risk,storing the issues, aggregating the issues, filtering the issues byexecuting a predefined rule set to determine a set of issues foranalysis, creating a report including the set of issues for analysis,and transmitting the report to a user.” However, Smoot et al. does notaddress issues from dynamic and defined sources, such as informationfrom current events.

As another example, Anne et al., U.S. Patent Application PublicationNumber 2014/0052494, describes “identifying scenarios and business unitsthat benefit from scenario planning for operational risk scenarioanalysis using analytical and quantitative methods.” Anne et al.describes that “a historical heat loss heat map may be utilized to showpast historical pain points and loss recovery rate information.”However, Anne et al. does not address utilizing dynamic and definedsources, such as information from current events.

Furthermore, many other conventional approaches rely heavily onhistorical analyses and monitoring of action items related to theanalyses. Thus, new trends, emerging trends, and other unknown risks maynot be appropriately identified and applied to risk models.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate machine learning toprovide incremental static program analysis.

Scenario planning can be used by businesses and governments to developlong term future plans. Scenario planning for risk management puts addedemphasis on identifying the extreme but possible risks that are notusually considered in daily operations. While a variety of methods andtools have been proposed for this purpose, embodiments of thisdisclosure provide formulating of planning techniques that comprise aunique advantage for scenario planning According to this disclosure,systems, computer-implemented methods, computer program products, andother implementations can receive as input relevant news, social mediatrends, and other input from defined sources that characterize thecurrent situation.

In response to the input, this disclosure provides for receivingselections of representative key observations, as well as a novel riskdriver model developed by risk management experts in a graphical tool.An artificial intelligence planner component can also be used togenerate multiple artificial intelligence plans explaining theobservations and projecting future states. The resulting plans can thenbe clustered and summarized to generate scenarios for use in scenarioplanning and risk management.

Accordingly, embodiments of this disclosure can analyze current trends,current information, current news, current key risk drivers, expertknowledge, expert observations, expert feedback, and other relatedattributes to develop and predict future scenarios that can solvedrawbacks related to current systems. Furthermore, embodiments of thisdisclosure can comprise novel feedback mechanisms that can refine thepredicted future scenarios based on real-time data and expert feedback.

According to an embodiment, a computer-implemented method is provided.The computer-implemented method can comprise analyzing, by a deviceoperatively coupled to a processor, content using a topic model. Thecontent can be associated with a defined source and is related to one ormore current events. The computer-implemented method can also comprisedetermining, by the device, one or more portions of the analyzed contentthat are relevant to one or more key risk drivers using a risk drivermodel. The computer-implemented method can also comprise aggregating, bythe device, the determined one or more portions into one or moreemerging storylines based on values of one or more attributes of thetopic model. It follows that the emerging storylines can compriseup-to-date information related to a monitored risk management scenario.Furthermore, the emerging storylines can also take into considerationinformation from several current and defined sources, thereby increasingthe likelihood that an associated future scenario and associated risk isaccurate.

According to another embodiment, the defined source can be selected fromthe group consisting of news sources, web-crawling systems, userpostings on social media, and trending topics on social media. Thus,emerging trends based on a plurality of defined sources can beconsidered in real-time, or in a relatively rapid manner. Theconsideration in real-time, or in a relatively rapid manner, allows formore accurate predictions of future scenarios and emerging storylines.

According to another embodiment, the topic model can comprise key peopleor organizations that are able to influence at least one key risk driveridentified in the risk driver model. Through consideration of key peopleor organizations that can influence at least one key risk driver,emerging storylines are more likely to be relevant to a central conceptor aspect of the scenario planning. Furthermore, the emerging storylinesare also more likely to take into consideration currently relevant riskdrivers that can affect a future scenario. Thus, risk mitigation,emerging risk, and/or associated business implications are also morelikely to be accurate.

According to another embodiment, the computer-implemented method cancomprise receiving, by the device, a selection of a key risk driver.Further, the computer-implemented method can comprise generating, by thedevice, one or more future scenarios using the selection of the key riskdriver. Additionally, the one or more future scenarios can comprise ascenario from the group consisting of a probable emerging risk, apossible emerging risk, and an associated business implication.Accordingly, through receipt of a selection of a key risk driver, theone or more future scenarios are more likely to be accurate.Furthermore, through receipt of a selection of a key risk driver, thisdisclosure provides for the benefit of considering current events andrelevant key risk drivers identified by experts in a particular field.The considering current events and relevant key risk drivers can providefor increased accuracy and refined future scenarios.

According to another embodiment, a computer-implemented method isprovided. The computer-implemented method can comprise receiving, by adevice operatively coupled to a processor, a risk driver model andreceiving, by the device, a topic model. The computer-implemented methodcan further comprise receiving, by the device, content from a definedsource. The content can be related to one or more current events. Thecomputer-implemented method can also comprise analyzing, by the device,the received content using the topic model and determining, by thedevice, one or more portions of the analyzed content that are relevantto one or more key risk drivers based on the risk driver model. Thecomputer-implemented method can also comprise outputting, by the device,the determined one or more portions as an emerging storyline based onvalues of one or more attributes of the topic model. Thecomputer-implemented method can also comprise receiving, by the device,a selection of a key risk drivers responsive to outputting the emergingstoryline, generating, by the device, one or more future scenarios usingthe selection of the key risk drivers, and outputting, by the device,the generated one or more future scenarios.

It follows that as the emerging storylines can comprise informationrelated current events, that a monitored risk management scenario ismore likely to be accurately reflected in the outputted one or morefuture scenarios. Furthermore, the emerging storylines can also takeinto consideration information from several sources, thereby increasingthe likelihood that an associated future scenario and associated risk isaccurate. Furthermore, the receipt of a selection of a key risk drivercan allow for expert knowledge to be integrated in the outputted one ormore future scenarios.

According to another embodiment, the computer-implemented method canalso comprise receiving a selection of a second key risk driverresponsive to the outputted one or more future scenarios, refining thegenerated one or more future scenarios based on the second key riskdriver into a new set of one or more future scenarios, and outputtingthe new set of one or more future scenarios. Thus, according to thisexample, the second key risk driver can provide for additional expertknowledge to be integrated in the new set of one or more futurescenarios.

According to another embodiment, the generated one or more futurescenarios can comprise a first future scenario. Additionally, thecomputer-implemented method can comprise iteratively refining the firstfuture scenario based on received feedback to create a refined futurescenario. According to this example, the refined future scenario isindicative of emerging risk. Thus, the refined future scenario can bemore likely to accurately reflect the emerging risk based on theplurality of defined sources and the iterative refining based onselection of key risk driver.

According to yet another embodiment, the computer-implemented method cancomprise receiving a final assessment of emerging risk and mitigationactions responsive to the outputted one or more future scenarios.Therefore, the final assessment is more likely to take intoconsideration current, expert knowledge and current events. Thus, thefinal assessment is likely to accurately reflect emerging risk.

In some embodiments, elements described in connection with thecomputer-implemented can be embodied in different forms such as one ormore program instructions stored on a computer readable storage medium,a computer program product, a system, or another form.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat can provide scenario planning in accordance with one or moreembodiments of the disclosed subject matter.

FIG. 2 illustrates a block diagram of an example, non-limiting systemthat can provide scenario planning in accordance with one or moreembodiments of the disclosed subject matter.

FIG. 3 illustrates a flowchart of an example, non-limitingcomputer-implemented method of scenario planning in accordance with oneor more embodiments of the disclosed subject matter.

FIG. 4 illustrates a flowchart of an example, non-limitingcomputer-implemented method of scenario planning in accordance with oneor more embodiments of the disclosed subject matter.

FIGS. 5A and 5B illustrate a flowchart of an example, non-limitingcomputer-implemented method of scenario planning in accordance with oneor more embodiments of the disclosed subject matter.

FIG. 6 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 7 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 8 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 9 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 10 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

This disclosure relates in some embodiments to scenario planning, andmore specifically, to artificial intelligence based solutions toscenario planning and/or risk management. Scenario planning as relatedto projected scenarios can facilitate strategic planning Scenarioplanning as described herein can involve analyzing the relationshipbetween risk drivers that explain the current situation in addition toproposing insights about the future thereby incorporating a majorbenefit of current knowledge and expertise to scenario planning. Whileexpected scenarios can be interesting for verification purposes,scenarios that are surprising to a user can also be relativelysignificant. For example, surprising scenarios can be used to developand/or assess varying forms of risk management.

Risk management is a set of principles that focus on the outcome ofrisk-taking. In this disclosure, scenario planning for risk managementis addressed. Furthermore, the problem of generating scenarios with asignificant focus on identifying the extreme but possible risks that arenot usually considered in daily operations is also addressed. Severalembodiments of this disclosure can provide the benefit of consideringemerging risks based on observations from several defined sources ofnews and social media trends, and provide scenarios which describe thecurrent situation and project the future possible effects of theseobservations. Furthermore, future projected scenarios are facilitatedthat can highlight potential leading indicators (e.g., the set of factsthat are likely to cause a scenario), in addition to any implications(e.g., effects that a certain organization cares about).

In this disclosure, useful and novel graphical presentations of scenarioplanning for risk management are provided. Furthermore, the processingof new or current information is also provided in a manner thatfacilitates beneficial inclusion in risk management models. Through thegraphical presentations of the scenario planning, feedback from experts,analysts, and other similar users can also be included in riskmanagement models and possible future scenarios. Thus, some embodimentscan also refine possible future scenarios based on both data fromdefined sources and expert knowledge. These and other features of thisdisclosure can facilitate the consideration of complexities that gobeyond average human skill, with increased accuracy, and decreased timein producing a useful result.

Turning now to the drawings, FIG. 1 illustrates a block diagram of anexample, non-limiting system 100 that can provide scenario planning inaccordance with one or more embodiments of the disclosed subject matter.The system 100 can include a data aggregation component 106, and ascenario projection component 110 in electrical communication with thedata aggregation component 106. As shown, the data aggregation component106 can include one or more processing units 108 arranged to providefunctionality described herein. Similarly, the scenario projectioncomponent 110 can include one or more processing units 112 arranged toprovide functionality described herein. The system 100 can also includea plurality of defined sources 102 in electrical communication with thedata aggregation component 106 via a network 104. The plurality ofdefined sources 102 can include, for example, defined source 102 ₁,defined source 102 ₂, and defined source 102 _(N). Other arrangements ofthe individual illustrated components of the system 100 can also beapplicable. Therefore, the particular arrangement illustrated is onenon-limiting example. Repetitive description of like elements employedin other embodiments described herein is omitted for sake of brevity.

The data aggregation component 106 can include defined hardware (e.g.,the processing units 108) configured to automate several programinstructions described herein. The data aggregation component 106 can bearranged to receive a topic model 114 from an input device, anelectronic source, or any suitable source. The topic model 114 can alsobe received from a domain expert, or from another suitable source.According to one embodiment, the topic model 114 can include a list ofimportant people, organizations, and keywords. Generally, the dataaggregation component 106 can be configured to aggregate data and/orcompile a set of relevant key observations from the data.

The scenario projection component 110 can include defined hardware(e.g., the processing units 112) configured to automate several programinstructions described herein. The scenario projection component 110 canbe arranged to receive a risk driver model 116 from an input device, anelectronic source, or any suitable source. The risk driver model 116 canalso be received from a domain expert, or from another suitable source.According to one embodiment, the risk driver model 116 is a descriptionof the causes and effects for a particular risk driver (e.g., a centralconcept). The risk driver model 116 can also include one or more contentmonitoring topics corresponding to a leading indicator of a key riskdriver. In this example, the leading indicator is a key risk driver,where as it is a leading indicator of another risk driver that is notnecessarily a key risk driver. Thus, a convention is followed where whatis presented and selected with the emerging storylines is a key riskdriver, and what is in the risk driver model is simply a risk driver. Inthis framework any of the risk drivers could become a key risk driver assoon as it is shown together with an emerging storyline, or when aparticular risk driver is selected. According to some embodiments, therisk driver model 116 can be captured through use of a graphical userinterface or another input tool. The graphical user interface canfacilitate the encoding of concepts and relations that encompass therisk driver model 116.

The scenario projection component 110 can also be arranged to receiveexpert selections 118 from an input device, an electronic source, or anysuitable source. The expert selections 118 can also be received throughthe use of a graphical user interface. According to one embodiment, theexpert selections can include, but are not limited to, selections ofpotential likelihoods and impact of a cause or an effect, selections ofone or more key risk drivers, selections of one or more leadingindicators, or other suitable information. Generally, the scenarioprojection component 110 can be configured to automatically generate anartificial intelligence planning problem. Additionally, an outcome ofthe artificial intelligence planning problem can be used to generate aset of alternative and/or projected future scenarios.

The system 100 can be deployed for any organization and can be deployedacross any suitable hardware capable of receiving data from theplurality of defined sources 102. According to one embodiment, a definedsource can be selected from the group consisting of news sources,web-crawling systems, user postings on social media, and trending topicson social media. Other defined sources are also contemplated, and theseexamples should not be construed as limiting.

Hereinafter, detailed discussion of definitions and concepts useful forunderstanding the operation of system 100 are described in detail.Generally, a scenario planning for risk management problem can berepresented based on three definitions, provided in Definition 1,Definition 2, and Definition 3, shown below.

Definition 1: An artificial intelligence planning problem is a tupleP=(F, A, I, G), where F is a finite set of fluent symbols, A is a set ofactions with preconditions, PRE(a), add effects, ADD(a), delete effects,DEL(a), and action costs, COST(a), I⊆F defines the initial state, andG⊆F defines the goal state.

According to Definition 1, a state, s, is a set of fluents that aretrue. An action a is executable in a state s if PRE(a)⊆s. The successorstate is defined as δ(a, s)=((s\DEL(a)) ∪ADD(a)) for the executableactions. The sequence of actions π=[a₀, . . . , a_(n)] is executable ins if state sf=δ(a_(n), δ(a_(n-1), . . . , δ(a₀, s))) is defined.Moreover, π is the solution to P if it is executable from the initialstate and G⊆δ(a_(n), δ(a_(n-1), . . . , δ(a₀, I))).

As relayed in Definition 1, the term “fluents” refers to fluentconditions and/or other conditions that change over time. Fluentconditions are conditions that change over time, and can include avariety of conditions associated with the domain knowledge representedby the graphical representation of domain knowledge 100. These fluentconditions can include, for example, degradation of an object, trafficwithin a loading dock, weather conditions over a defined geography, andother suitable conditions. Other fluent conditions and/or conditionsthat change over time are also contemplated, and the examples providedherein are not to be construed as limiting in any manner.

Furthermore, an artificial intelligence plan recognition problem forprocessing by an artificial intelligence planning and recognitioncomponent can be represented as set forth in Definition 2, below.

Definition 2: An artificial intelligence plan recognition problem is atuple PR=(F, A, I, O, G, PROB), where (F, A, I) is the planning domainas defined in Definition 1 above, 0=[o1, . . . , om], where oi∈F, i∈[1,m] is the sequence of observations, G is the set of possible goals G,G⊆F, and PROB is the goal priors, P (G).

According to Definition 2, unreliable, unexplainable, or otherwise noisyobservations are defined as those that have not been added to the stateas a result of an effect of any of the actions in a plan for aparticular goal, while missing observations are those that are added tothe state but are not observed (i.e., are not part of the observationsequence). To address the noisy observations, the definition ofsatisfaction of an observation sequence by an action sequence ismodified to allow for observations to be left unexplained. Given anexecution trace and an action sequence, an observation sequence is saidto be satisfied by an action sequence and its execution trace if thereis a non-decreasing function that maps the observation indices into thestate indices as either explained or discarded.

Definition 3: A scenario planning for risk management problem is definedas a tuple SP=(F, A, I, O, I), where (F, A, I) is the planning domain asdefined above, O=[o₁, . . . , o_(m)], where of ∈F, i∈[1, m] is anordered sequence of observations or key drivers, F is a set of goals orbusiness implications G⊆F.

Turning back to FIG. 1, general input to the system 100 can include rawsocial media posts and news articles, content from the Internet, and/orother information retrievable over the network 104. The data aggregationcomponent 106 can analyze the input data to facilitate generation of asolution to the scenario planning for risk management problem definedabove by the scenario projection component 110.

The solution to the scenario planning for risk management problem isdefined as a set of scenarios, where each scenario is a collection ofplans II such that: (i) each plan π=[a₀, . . . , a_(i), a_(i+) ₁ , . . ., a_(n)] is an action sequence that is executable from the initial stateI and results in state s=δ(a_(n), . . . , δ(a₀, I), (ii) at least one ofthe goals is met (i.e., ∃G ∈Γ, where G ⊆s), and (iii) the sequence ofobservations is satisfied by the action sequence [a₀, . . . , a_(i)].The scenario planning for risk management problem can be thought of as aplan recognition problem, where observations and a set of goals aregiven. Rather than computing P(π|O) and P(G|O), the solution to thescenario planning for risk management problem is a set of scenariosshowcasing alternative possible outcomes, akin to computing a set ofdiverse plans.

Accordingly, a framework for determining a solution to a scenario forrisk management problem has been defined. Hereinafter, operationalcharacteristics of the system 100 are described in detail beginning withinput and capture of domain knowledge via the risk driver model 116.

Generally, knowledge engineering tools assume that a domain expert hassome artificial intelligence planning background. However, according tosome embodiments, the disadvantage of unknowledgeable domain experts inartificial intelligence planning is circumvented through intelligentacquisition of domain knowledge in an automated manner. For example,according to some embodiments, a domain expert can express the knowledgein a light-weight graphical user interface and the system 100 (e.g., thescenario projection component 110) can automatically translate thedomain knowledge. The form of encoded domain knowledge and translationis explained below.

As shown in FIG. 1, domain knowledge can be received as risk drivermodel 116 and expert selections 118. For example, the risk driver model116 can include domain knowledge corresponding to the causes and effectsof the different risk drivers influencing the risks in an organization(e.g., the economy, currency, politics, and/or social unrest). A mindmap is a graphical representation of the domain knowledge that can beused to express the risk driver model in a simplified mannerHereinafter, the capture of domain knowledge in an automated mannerusing a graphical representation of domain knowledge is described indetail with reference to FIG. 6.

FIG. 6 illustrates a block diagram of an example, non-limiting graphicaluser interface 650 in accordance with one or more embodiments of thedisclosed subject matter. As illustrated, the graphical user interface650 can include a graphical representation of domain knowledge 600,presented for and editable by a user. The graphical user interface 650can be generated by a processing device and presented via an outputdevice such as, for example, a computer monitor or other display device.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for the sake of brevity.

The graphical representation of domain knowledge 600 can comprise acentral concept 602, leading indicator 604, leading indicator 606,leading indicator 608, implication 610, implication 612, and implication614. The graphical representation of domain knowledge 600 can furthercomprise implication 616 associated with implication 616, weight 622associated with leading indicator 604, and weight 624 associated withimplication 614.

It should be understood that the particular arrangement of the graphicalrepresentation of domain knowledge 600 can change according toassociated domain knowledge. For example, more or fewer leadingindicators, weights, and/or implications can be included according toany particular implementation and associated domain knowledge.Additionally, more or fewer weights associated with any number ofleading indicators and/or implications can also be applicable.Accordingly, the particular form of the graphical representation ofdomain knowledge 600 should not be considered limiting in any way.

As shown in FIG. 6, the central concept 602 is a general conceptrepresentative of a risk driver in an associated domain. For example, apossible risk driver can include a “low oil price”. Other possible riskdrivers can also be applicable to particular domains. Accordingly,exhaustive description of a plurality of different risk drivers andcentral concepts is omitted herein for the sake of brevity. Generally,through manipulation of the graphical user interface 650, the centralconcept 602 can be manipulated, altered, changed, and/or updated.

As further shown in FIG. 6, there are several arrows providing fordirectionality to and from the central concept 602. From the arrows,several traits of the domain knowledge can be inferred. For example,arrows directed towards the central concept 602 can be indicative of asource concept being a “leading indicator”. Furthermore, arrows directedfrom the central concept 602 can be indicative of a possible“implication”. Moreover, several concepts can be tied together toestablish a chain of events leading to or from the central concept inany desirable manner Thus, the particular arrangements of arrowsillustrated in FIG. 6 are merely examples of one possible graphicalrepresentation of particular domain knowledge, and therefore should notbe construed as limiting. Generally, through manipulation of thegraphical user interface 650, the arrows can be manipulated, altered,changed, and/or updated.

As further shown in FIG. 6, the central concept 602 can be associatedwith leading indicator 604, leading indicator 606, and leading indicator608. The leading indicators can represent a portion of the domainknowledge of the graphical representation of domain knowledge 600. Theleading indicators can comprise concepts that can affect the centralconcept 602. For example, and without limitation, continuing the exampleof a central concept of a “low oil price,” a possible leading indicatorcan include a “warmer than normal winter in the northern hemisphere.”Thus, if this leading indicator were true, a resulting implication mayresult that affects the central concept. Generally, through manipulationof the graphical user interface 650, the leading indicators can bemanipulated, altered, changed, and/or updated.

As further shown in FIG. 6, the central concept 602 can be associatedwith implication 610, implication 612, and implication 614.Additionally, implication 616 can be associated with implication 614.The implications may represent a portion of the domain knowledge of thegraphical representation of domain knowledge 600. The implications caninclude concepts or implications affected by the leading indicators andany possible occurrence. For example, and without limitation, continuingthe example of a central concept of a “low oil price,” a possibleimplication can include “oil prices continue to rise.” Thus, if theexample leading indicator of a “warmer than normal winter in thenorthern hemisphere” were true, the possible implication of “oil pricescontinue to rise” may also be true as an implication of the leadingindicator and central concept. Furthermore, as illustrated in FIG. 6,under some circumstances, implication 616 may result as a link in achain of events beginning with a particular leading indicator beingtrue, that particular leading indicator causing implication 614, andimplication 614 therefore causing implication 616. Other chains ofevents can also be applicable under some circumstances. Furthermore,through manipulation of the graphical user interface 650, theimplications can be manipulated, altered, changed, and/or updated.

As further shown in FIG. 6, leading indicator 604 can include a weight622 associated the leading indicator 604. Additionally, the implication614 can include a weight 624 associated with the implication 614.Generally, the weight 622 and the weight 624 can represent an actioncost or other similar attribute. The action cost can indicate a cost ofperforming an action associated with the leading indicator 604 and/orthe implication 614. Furthermore, although not illustrated for clarity,according to at least one embodiment, every leading indicator caninclude an associated weight or cost. Additionally, according to atleast one embodiment, every implication can include an associated weightor cost. Similarly, according to some embodiments, at least one or moreleading indicators does not have an associated weight or cost. Moreover,according to some embodiments, at least one or more implications doesnot have an associated weight or cost. Additionally, throughmanipulation of the graphical user interface 650, the weights can bemanipulated, altered, changed, and/or updated. Thus, the particulararrangement of the graphical representation of domain knowledge 600 canbe varied and manipulated in many ways.

As described above, the graphical representation of domain knowledge 600captures domain knowledge from experts and/or other users. The domainknowledge encoded through manipulation of the graphical user interface650 can also be automatically translated into an artificial intelligencedescription language. For example, an action can be defined for eachcause, as well as each edge in the graphical representation of domainknowledge 600. The translation can be performed on-the-fly through useof the graphical user interface 650, or can be post-processed (e.g.,through the scenario projection component 110).

In order to use artificial intelligence planning to determine a solutionto the scenario planning for risk management problem, observations arecompiled away. To do so a set of “explain” and “discard” actions can beadded for each observation. The discard action can be selected in orderto leave some observations unexplained. To encourage an artificialintelligence planning and recognition component to generate plans thatexplain as many observations as possible, a penalty is given for the“discard” action in the form of costs. The penalty is relative to thecost of the other action in the domain. To compute a set of high-qualityplans, a top-k planning approach can be implemented. Top-k planning isdefined in as the problem of finding k set of plans that have thehighest quality. A known algorithm to compute the set of top-k plans isbased on the k shortest paths algorithm called K^(L) which also allowsuse of heuristics search. The K^(L) algorithm together with the LM-cutheuristic can be implemented in the system 100 of FIG. 1 (e.g., throughscenario projection component 110).

Hereinafter, post-processing of the domain knowledge generation ofpossible future scenarios that can be facilitated by the system 100 isdescribed in detail.

To compute possible future scenarios, a set of post-processing steps onthe computed set of plans can be performed by the system 100. Initially,a set of unique plans is determined. For the purpose of scenarioplanning, predicates directly derived from the concepts of the riskdriver model 116 and/or the graphical representation of domain knowledge600 are retained. Therefore, plans that are distinct to an implementedartificial intelligence planner and recognition component can beconsidered equivalent for scenario planning purposes.

Responsive to generating the unique plans, the resulting plans can beclustered as described above to create possible future scenarios. Theplans can be clustered according to the predicates present in the laststate as encoded in the risk driver model 116. Since the number ofground predicates is generally finite, each plan can be identifiedthrough a bit array of the same size as the number of ground predicates,in which “1” indicates the predicate is present. To determine theEuclidian distance between two plans, a logical OR can be computed ofthe corresponding bit arrays and the square root of the number of “1”bits can be computed.

With this distance, a case of “opposite” predicates (e.g.,weakening/strengthening economic environment, increase/decrease ininflation, etc.) is contended. To ensure avoidance of plans withopposite predicates ending up in the same cluster, a penalty factor isadded to the number of “1” bits utilized to compute the distance forevery pair of opposite predicates found in the plans. Given thisdistance function, a dendrogram bottom-up is computed using thecomplete-linkage clustering method with the mean of all distancesbetween clusters as the aggregate function. A user can specify a minimumand maximum consumable number of scenarios using the graphical userinterfaces and input devices described herein. The specified minimum andmaximum settings can be used to perform a cut through the dendrogramthat yields a number of clusters in the given interval and has theoptimal Dunn index.

After post-processing is complete, several tasks can be performed toprepare the projected future scenarios for presentation. Predicates canbe separated in each cluster (e.g., projected scenario) intoimplications and regular predicates. At the same time, probable andpossible predicates can be separated in each of these categories bydetermining the proportion of plans where the predicate is present inthe last state from all plans in the scenario. Additionally, a set ofleading indicators can be computed based on the risk driver model 116.The computed set of leading indicators are predicates that appear earlyon the plans that are part of one scenario and are also discriminating(i.e., they tend to lead to this scenario and not others). The computedset of leading indicators is useful to monitor in order to determineearly on whether a projected scenario is likely to occur. Additionally,a summary of all plans that are part of the current projected scenariocan be output as future scenario data 120.

As described above, the system 100 can include a data aggregationcomponent 106 in electrical communication with a scenario projectioncomponent 110. Furthermore, the system 100 can be configured to performseveral methodologies to compute solutions to a scenario planning forrisk management problem and output one or more projected futurescenarios. Hereinafter, additional embodiments of systems and methods ofscenario planning are described in detail.

Turning back to the drawings, FIG. 2 illustrates a block diagram of anexample, non-limiting system 200 that can provide scenario planning inaccordance with one or more embodiments of the disclosed subject matter.The system 200 can include the data aggregation component 106 inelectrical communication with the scenario projection component 110, andother elements similar to system 100. As further illustrated, the dataaggregation component 106 can be arranged to include an ingest adaptercomponent 202, a text analytics component 204 in electricalcommunication with the ingest adapter component 202, a topic model store206 in electrical communication with the text analytics component 204, astoryline database 208 in electrical communication with the textanalytics component 204, a topic model processing component 214 inelectrical communication with the topic model store 206, a dataaggregation server component 212 in electrical communication with thestoryline database 208, and a cache database 210 in electricalcommunication with the data aggregation server component 212.Furthermore, the scenario projection component 110 can be arranged toinclude a risk driver store 216, a risk impact profile server component226 in electrical communication with the risk driver store 216, a riskimpact store in communication with the risk profile server component226, a scenario planning component 220 in electrical communication withthe risk driver store 216, and an artificial intelligence planning andrecognition component 224 in electrical communication with the scenarioplanning component 220. Additionally, the scenario planning component220 can be in electrical communication with the risk impact store 218and the data aggregation component 106. Repetitive description of likeelements employed in other embodiments described herein is omitted forthe sake of brevity.

According to one or more embodiments, the ingest adapter component 202,the text analytics component 204, the topic model processing component214, and the data aggregation server component 212, can be arranged toperform any of the functionality described above with reference to thedata aggregation component 106. Furthermore, according to one or moreembodiments, the ingest adapter component 202, the text analyticscomponent 204, the topic model processing component 214, and the dataaggregation server component 212 can also be implemented as a discreteset of program instructions that implement any portion of the describedmethodologies in any ordered sequence.

According to one or more embodiments, the topic model store 206, thestoryline database 208, the cache database 210, the risk driver store216, and the risk impact store 218 can be arranged as databasecomponents or storage components. Additionally, the topic model store206, the storyline database 208, the cache database 210, the risk driverstore 216, and the risk impact store 218 can be arranged as discretestorage components on a single or multiple storage media.

According to one or more embodiments, the risk impact profile servercomponent 226, the scenario planning component 220, and the artificialintelligence planning and recognition component 224 can be arranged toperform any of the functionality described above with reference to thescenario projection component 110. Furthermore, according to one or moreembodiments, the risk impact profile server component 226, the scenarioplanning component 220, and the artificial intelligence planning andrecognition component 224 can also be implemented as a discrete set ofprogram instructions that implement any portion of the describedmethodologies in any ordered sequence.

With reference to the system 200, a scenario planning methodology can beexecuted that can generate one or more future scenarios useful in riskmanagement. For example, the one or more future scenarios can begenerated generally as described above. Hereinafter, a more detaileddescription of the operation of the system 200 is provided.

In scenario planning as described herein, it can be beneficial toutilize and leverage knowledge provided by experts as well as contentfrom one or more defined sources. For example, one or more domainexperts can create a risk driver model 116 by describing risk driversdriving the domain and their effects and leading indicators, andassessing risk driver impact in different regions. The risk driver model116 can be stored at the risk driver store 216. Furthermore, the riskdriver model 116 can be processed by the risk impact profile servercomponent 226 to determine any associated risk impact or risk impacts.The associated risk impact or risk impacts can be stored at the riskimpact store 218.

Additionally, domain experts can create the topic model 114. The topicmodel 114 can describe key people and/or organizations that are able toinfluence at least one key risk driver identified in the risk drivermodel 116. The topic model 114 can also describe topics, key words,and/or other metadata for monitoring as related to the risk driver model116. The topic model 114 can be processed by the topic model processingcomponent 214. Furthermore, the topic model 114 can be stored at thetopic model store 206.

Responsive to processing and storing the topic model 114 and/or the riskdriver model 116, data from the network 104 can be continuously ingestedby the ingest adapter component 202. Responsive to the continuousingestion, the text analytics component 204 can analyze and process theingested data. For example, the text analytics component 204 can bearranged to continuously analyze data received from the defined sources102. Furthermore, the text analytics component 204 can locate emergingstorylines based on the topic model 114.

Responsive to locating the emerging storylines, the text analyticscomponent 204 can be arranged to store the emerging storylines at thestoryline database 208. The data aggregation server component 212 can bearranged to access the stored emerging storylines from the storylinedatabase 208. The data aggregation server component 212 can also bearranged to aggregate the retrieved emerging storylines based on thetopic model 114 and/or the risk driver model 116. Responsive toaggregation of the emerging storylines, the data aggregation servercomponent 212 can cache assembled data at the cache database 210, andpresent the cached data for review by domain experts and/or analysts.For example, domain experts and/or analysts can review the aggregatedstorylines and select key risk drivers (e.g., as expert selections 118)describing their observations.

Upon receiving expert selections 118, the scenario planning component220 can generate future scenario data 120. For example, scenarioplanning component 220 can generate possible future scenarios, based ona current situation and the risk impact model 116, taking into accountregional risk driver impact as accessible at the risk impact store 218.Furthermore, the artificial intelligence planning and recognitioncomponent 224 can operate as described above with reference toDefinition 1, Definition 2, and Definition 3. Therefore, the scenarioplanning component 220, using information received from the artificialintelligence planning and recognition component 224, can provide one ormore future scenarios for further review, refinement, and risk analysis.Upon receipt of the future scenario data 120, the domain experts and/oranalysts can review the generated scenarios and recommend actions basedon the generated scenarios. For example, the domain experts and/oranalysts can provide additional expert selections 118 via a graphicaluser interface, can review the future scenario data 120 via a graphicaluser interface, and/or can refine the risk driver model 116 and topicmodel 114 via a graphical user interface.

Through these interactions, the system 200 facilitates a robust solutionto scenario planning with many benefits. For example, through repeatedinteractions with domain experts and analysts, the future scenario data120 can be refined based on current events and observations made by thedomain experts and/or analysts. Additionally, the ingest adapter 202 andthe text analytics component 204 can continually monitor the definedsources 102 to provide up-to-date information on emerging trends. Thus,according to some embodiments, the future scenarios data 120 overcomesdrawbacks associated with mere historical analysis while also improvingaccuracy and reducing computation time for similar analyses.

Hereinafter, a more detailed explanation of the operation of the system100 and the system 200 is presented with discussion ofcomputer-implemented methodologies that can be executed by the system100 and/or the system 200.

FIG. 3 illustrates a flowchart of an example, non-limitingcomputer-implemented method 300 of scenario planning in accordance withone or more embodiments of the disclosed subject matter. The computerimplemented method 300 can include, for example, a sequence of programinstructions to be performed by a data aggregation component, such asthe data aggregation component 106. The computer implemented method 300can include, but is not limited to, blocks 302, 304, and 306. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for the sake of brevity.

According to one embodiment, the computer-implemented method 300 caninclude analyzing, by a device operatively coupled to a processor,content using a topic model, at block 302 (e.g., by the data aggregationcomponent 106). The content can be associated with a defined source andcan be related to one or more current events. For example, the topicmodel can be topic model 114 and the content can be retrieved orreceived from a plurality of defined sources 102. Accordingly, whenprocessed by the data aggregation component 106, the analyzing of thecontent from the plurality of defined sources 102 is performed in arapid and accurate manner (e.g., greater than the capability of a singlehuman mind). For example, an amount of data processed, a speed ofprocessing of data and/or data types processed by the data aggregationcomponent over a certain period of time can be greater, faster anddifferent than an amount, speed and data type that can be processed by asingle human mind over the same period of time.

The computer-implemented method 300 can further include determining, bythe device, one or more portions of the analyzed content that arerelevant to one or more key risk drivers using a risk driver model, atblock 304 (e.g., by the data aggregation component 106). In thisexample, topics from the topic model can be used in making decisionsabout candidate key risk drivers that are relevant. For example,relevant key risk drivers for the received content can be determinedbased on topics that are associated with the content by applying thetopic model. In following this example, topics that can result fromapplying the topic model to the content can be used to suggest candidatekey risk drivers for selection, where candidate key risk drivers can bepresented for selection via a graphical user interface, and theselection can be used to generate future scenarios.

The computer-implemented method 300 can also include aggregating, by thedevice, the determined one or more portions into one or more emergingstorylines based on values of one or more attributes of the topic model,at block 306 (e.g., by the data aggregation component 106). According toone embodiment, the aggregating can include combining the one or moreemerging storylines with one or more attributes extracted from thecontent. It is noted that, when processed by the data aggregationcomponent 106, the aggregating of the determined one or more portionsinto one or more emerging storylines is handled in a rapid and accuratemanner (e.g., is greater than the capability of a single human mind).For example, an amount of data processed, a speed of processing of dataand/or data types processed by the data aggregation component over acertain period of time can be greater, faster and different than anamount, speed and data type that can be processed by a single human mindover the same period of time.

Generally, the emerging storylines can include data related to the oneor more current events, and other data collected and/or received fromthe plurality of defined sources 102. This data can be processed basedon the risk driver model 116 to generate future scenario data 120.Accordingly, embodiments that include data collected and/or receivedfrom the plurality of defined sources 102 take into consideration andanalyze current trends, current information, current news, current keyrisk drivers, expert knowledge, expert observations, expert feedback,and other related attributes to develop and predict future scenariosthat go beyond mere historical analysis. Furthermore, the futurescenario data 120 can be refined based on expert selections 118. FIG. 4illustrates a flowchart of an example, non-limiting computer-implementedmethod 400 of scenario planning in accordance with one or moreembodiments of the disclosed subject matter. The computer implementedmethod 400 can include, for example, a sequence of program instructionsto be performed by a scenario projection component, such as the scenarioprojection component 110. The computer implemented method 400 caninclude, but is not limited to, blocks 402, 404, and 406. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for the sake of brevity.

The computer-implemented method 400 can include receiving, by thedevice, a selection of a key risk driver at block 402 (by the scenarioprojection component 110). The selection of the key risk driver caninclude expert selections 118, for example. Furthermore, the selectionof the key risk driver can include other selections or more than oneselection of a key risk driver. According to at least one embodiment, asecond key risk driver can also be received. Thus, according toembodiments including a selection of a key risk driver (e.g., block402), expert feedback can be continuously received, and can result inincreased accuracy of provided results.

Responsive to block 402, the computer-implemented method 400 can includegenerating, by the device, one or more future scenarios using theselection of the key risk driver, at block 404 (e.g., by the scenarioprojection component 110). For example, the scenario projectioncomponent 110 can process the risk driver model 116 and utilize theartificial intelligence planning and recognition component 224 togenerate the one or more future scenarios as described in detail above.Therefore, according to embodiments that include generating one or morefuture scenarios using the selection of the key risk driver (e.g., block404), expert feedback is used to generate a more accurate one or morefuture scenarios for risk management. Furthermore, when processed by thescenario projection component, the generation of the future scenariodata 120 occurs in a rapid manner (e.g., is greater than the capabilityof a single human mind). For example, an amount of data processed, aspeed of processing of data and/or data types processed by the scenarioprojection component over a certain period of time can be greater,faster and different than an amount, speed and data type that can beprocessed by a single human mind over the same period of time.

Responsive to block 404, the computer-implemented method 400 can alsoinclude outputting, by the device, the generated one or more futurescenarios (e.g., by the scenario projection component 106). As notedabove, the generation of one or more emerging storylines and outputtingof the generated one or more future scenarios is beneficial in thatfeedback can be received from domain experts and/or analysts. Therefore,the future scenario data 120 can be refined to increase accuracy ofpredictions.

For example, and without limitation, iterative refinement of futurescenario data 120 can be facilitated by the system 100 or the system200. Such iterative refinement is beneficial in that more accurateportrayals of risk management issues are now possible. FIGS. 5A and 5Billustrate a flowchart of an example, non-limiting computer-implementedmethod 500 of scenario planning in accordance with one or moreembodiments of the disclosed subject matter. The computer implementedmethod 500 can include, for example, a sequence of program instructionsto be performed by a data aggregation component and a scenarioprojection component, such as the data aggregation component 106 and thescenario projection component 110. The computer implemented method 500can include, but is not limited to, blocks 502, 504, 506, 508, 510, 514,516, 518, and 520. Repetitive description of like elements employed inother embodiments described herein is omitted for the sake of brevity.

The computer-implemented method 500 can include receiving, by a deviceoperatively coupled to a processor, a risk driver model, at block 502(e.g., by the scenario projection component 110). For example, the riskdriver model can be risk driver model 116. Generally, the risk drivermodel can be created by an expert. For example, the domain expert cancreate a list of key risk driver based on knowledge of the domain,identify leading indicators, effects, and business implications for eachkey risk driver, and define monitoring topics corresponding to theleading indicators of the risk drivers.

The computer-implemented method 500 can also include receiving, by thedevice, a topic model, at block 504 (e.g., by the data aggregationcomponent 106). For example, the topic model can be the topic model 114.Generally, the topic model can also be created by an expert. Forexample, the domain expert can provide a list of desired defined sources102, provide a list of key people and key organizations that are able toinfluence the key risk drivers, and provide a list of keywords formonitoring topics that are identified in the risk driver model.

An approval workflow for the topic model can also be implemented by ananalyst or any other person familiar with the system to review andrevise the changes to the elements of the topic model (e.g., people,organizations, and topic keywords), when these changes are made by adomain expert or an analyst, in order to implement improvements to thetopic model required to optimize news analysis performance. For example,the analyst can create a topic model template, based on the risk drivermodel, and include sample elements of the topic model, including sampleand preconfigured sources, topics, keywords, organizations and people.The template can be used for collecting further input related toelements of the topic model. Upon collecting the further input, thefurther input can be reviewed and modified as required to improveanalysis of content from the defined sources. Additionally, the analystcan approve any final revisions to the topic model for input and providethe most recent approved topic mode for use with thecomputer-implemented method 500.

In addition to the approval workflow described above, separate machinelearning techniques can be applied to the topic model for furtherimprovements. For example, an external knowledge corpus can be analyzed.The external knowledge corpus can include historical data includinghistorical data stores for the plurality of defined sources 102. Thisexternal knowledge corpus can then be analyzed by applying machinelearning, data mining, or other statistical techniques, and usingkeywords provided in the original topic model, in order to derive newkeywords, or names of people or organizations for inclusion in the topicmodel 114. Other additions and enhancements can also be applicable.Accordingly, these examples are not to be construed as limiting.

Turning back to FIG. 5, the computer-implemented method 500 can alsoinclude receiving, by the device, content from a defined source, atblock 506 (e.g., by the data aggregation component 106). For example,the content is related to one or more current events and the definedsource can be one or more of the plurality of defined sources 102.

Responsive to block 506, the computer-implemented method 500 can includeanalyzing, by the device, the received content using the topic model, atblock 508 (e.g., by the data aggregation component 106). As describedabove, the analyzing can be performed in a rapid, real-time, or almostreal-time manner (e.g., is greater than the capability of a single humanmind). For example, an amount of data processed, a speed of processingof data and/or data types processed by the data aggregation componentover a certain period of time can be greater, faster and different thanan amount, speed and data type that can be processed by a single humanmind over the same period of time.

Responsive to block 508, the computer-implemented method 500 can includedetermining, by the device, one or more portions of the analyzed contentthat are relevant to one or more key risk drivers based on the riskdriver model, at block 510 (e.g., by the data aggregation component106). The computer-implemented method 500 can further includeoutputting, by the device, the determined one or more portions as anemerging storyline based on values of one or more attributes of thetopic model, at block 514 (e.g., by the data aggregation component 106).As described above, processing to generate an emerging storyline and/oremerging storylines is complex and based on the topic model 114.Accordingly, when processed by the data aggregation component 106, thegeneration of the emerging storyline takes into consideration theplurality of defined sources 102, the topic model 114, and/or the riskimpact model 116 (e.g., is greater than the capability of a single humanmind). For example, an amount of data processed, a speed of processingof data and/or data types processed by the data aggregation componentover a certain period of time can be greater, faster and different thanan amount, speed and data type that can be processed by a single humanmind over the same period of time.

Responsive to outputting the emerging storylines, an analyst or domainexpert can review the emerging storylines and provide feedback. Forexample, the analyst can review the emerging storyline or emergingstorylines provided by the system 100 or the system 200. The analyst canalso select relevant key risk drivers or leading indicators from acandidate list associated with the emerging storylines. The analyst canalso review and revise any selections of key risk drivers and leadingindicators based on their understanding of the current situation.

Responsive to block 514, the computer-implemented method 500 can includereceiving, by the device, a selection of a key risk driver responsive tooutputting the emerging storyline, at block 516 (e.g., by scenarioprojection component 110). The computer-implemented method 500 can alsoinclude generating, by the device, one or more future scenarios usingthe selection of the key risk driver, at block 518 (e.g., by scenarioprojection component 110). The computer-implemented method 500 can alsoinclude outputting, by the device, the generated one or more futurescenarios, at block 520 (e.g., by scenario projection component 110).

Responsive to the outputting from block 520, an analyst or domain expertcan further refine the generated scenarios through iteration of any ofthe above methodologies. For example, an analyst can review theoutputted one or more future scenarios, and iterate through the abovedescribed blocks to refine the one or more future scenarios. If anyparticular scenario is of interest or is determined to be accurate, theanalyst can also prepare a final assessment of emerging risk andrecommend risk mitigation actions.

According to at least one embodiment, the computer-implemented method500 can also include receiving a selection of a second key risk driverresponsive to the outputted one or more future scenarios, refining thegenerated one or more future scenarios based on the second key riskdriver into a new set of one or more future scenarios, and outputtingthe new set of one or more future scenarios. In this example, theiterative refinement based on the selection of the second key riskdriver can result in benefits including improved accuracy with arelatively small increase in processing time.

According to an additional embodiment, the generated one or more futurescenarios comprise a first future scenario, and the computer-implementedmethod 500 further includes iteratively refining the first futurescenario based on received feedback to create a refined future scenario.In this example, the refined future scenario is indicative of emergingrisk. Thus, when the computer-implemented method 500 includes theiteratively refining the first future scenario, the first futurescenario takes into consideration the received feedback and observationsof domain experts, and can result in further improved accuracy.

As described above, several computer-implemented methods for scenarioplanning have been provided. The computer-implemented methods can beexecuted by individual components described in detail herein.Additionally, the computer-implemented methods can be encoded as programinstructions on a computer readable storage medium, a computer programproduct, and/or other articles of manufacture. As further describedabove, graphical user interfaces can be provided to facilitateinteractions between the system 100, the system 200, and/or domainexperts and analysts.

FIG. 7 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter. As illustrated, the graphical user interface750 can include portion 702, portion 704, and/or portion 706, presentedfor and editable by a user. The graphical user interface 750 can begenerated by a processing device and presented via an output device suchas, for example, a computer monitor or other presentation device.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for the sake of brevity.

Generally, the graphical user interface 750 is arranged as a topic modeluser interface to facilitate generation, editing, and receipt of topicmodel 114 or other suitable topic models. For example, the portion 702can include user interface components for selection of key people thatare able to influence at least one key risk driver identified in therisk driver model. Furthermore, the portion 704 can include userinterface components for selection of key organizations that are able toinfluence at least one key risk driver identified in the risk drivermodel. Furthermore, the portion 706 can include user interfacecomponents for selection of topics, key words, and/or other metadata formonitoring. It is noted that the particular arrangement of the portionsof graphical user interface 750 is non-limiting, and that the graphicaluser interface 750 can take multiple other forms.

FIG. 8 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter. As illustrated, the graphical user interface850 can include portion 802, portion 804, portion 806, portion 808,portion 810, and portion 812, presented for and editable by a user. Thegraphical user interface 850 can be generated by a processing device andpresented via an output device such as, for example, a computer monitoror other presentation device. Repetitive description of like elementsemployed in other embodiments described herein is omitted for the sakeof brevity.

Generally, the graphical user interface 850 is arranged as an emergingstoryline user interface to facilitate presentation of emergingstorylines and receipt of expert selections. For example, the portion802, the portion 804, and the portion 806 can present one or moreemerging storylines based on values of one or more attributes of thetopic model. Furthermore, the portion 808, the portion 810, and theportion 812 can include user interface components for selection of a keyrisk driver for expert selections 118. In at least one embodiment,candidate key risk drivers can also be presented through the portion812. It is noted that the particular arrangement of the portions ofgraphical user interface 850 is non-limiting, and that the graphicaluser interface 850 can take multiple other forms.

FIG. 9 illustrates a block diagram of an example, non-limiting graphicaluser interface in accordance with one or more embodiments of thedisclosed subject matter. As illustrated, the graphical user interface950 can include portion 902, portion 904, portion 906, portion 908,portion 910, portion 912, portion 914, portion 916, and portion 918,presented for and editable by a user. The graphical user interface 950can be generated by a processing device and presented via an outputdevice such as, for example, a computer monitor or other presentationdevice. Repetitive description of like elements employed in otherembodiments described herein is omitted for the sake of brevity.

Generally, the graphical user interface 950 is arranged as futureprojected scenario user interface to facilitate presentation of leadingindicators, scenarios and emerging risks, implications, and othersuitable information. For example, the portion 902, the portion 908, andthe portion 914 can present one or more leading indicators extractedfrom the risk driver model. Furthermore, the portion 904, the portion910, and the portion 916 present one or more possible future scenariosand/or emerging risks. Additionally, the portion 906, the portion 912,and the portion 918 present one or more possible implications. It isnoted that the particular arrangement of the portions of graphical userinterface 950 is non-limiting, and that the graphical user interface 950can take multiple other forms.

As described above, an artificial intelligence driven solution toscenario planning has been provided through the various embodimentsdescribed herein. Embodiments described in this disclosure can include,for example, a computer-implemented method and a system 100 that canprovide the described solution. The computer-implemented method cancomprise analyzing, by a device operatively coupled to a processor,content using a topic model. The content can be associated with adefined source and is related to one or more current events. Thecomputer-implemented method can also comprise determining, by thedevice, one or more portions of the analyzed content that are relevantto one or more key risk drivers using a risk driver model. Thecomputer-implemented method can also comprise aggregating, by theelectronic device, the determined one or more portions into one or moreemerging storylines based on values of one or more attributes of thetopic model.

The aggregated emerging storylines are advantageous in that the emergingstorylines take into consideration current expert knowledge, currentevents, current news data, current social media data, current socialmedia trends, and other current information. Through the processing ofinformation received from these defined sources, a technical benefitexists in that less user interaction can be necessary for generating asimilarly robust view of emerging risk or future scenarios. Thus, theaggregated emerging storylines are advantageous in that several currentsources of information are considered in a relatively rapid manner.

Additionally, embodiments have been described that can implementscenario planning in support of risk management, where the emergingrisks can be identified from one or more future scenarios that aregenerated by reasoning about the key risk drivers that drive theemerging risks, leading indicators of emerging risks, effects, and thebusiness implications associated with the emerging risks. As described,scenario planning according to some embodiments can include carrying outthis reasoning in the context of the emerging storylines derived frominput received from the defined sources.

Accordingly, some embodiments can facilitate the identification of thepossible future scenarios and therefore the possible emerging risks thatmay not be immediately identifiable without following rigorous analysis.The identification of the possible future scenarios can further helpidentify business insights related to these emerging risks. In addition,the identification of the possible future scenarios can allow foreffective knowledge capture and sharing. Furthermore, the identificationof the possible future scenarios can facilitate the consideration ofcomplexities that go beyond average human skill. The consideration ofthe complexities in identifying possible future scenarios thereforesaves time, reduces complexity, and increases accuracy through use offormal reasoning steps that can be implemented by a computer, and hencecan improve the efficiency and the effectiveness of the risk management.

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 10, the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), or the like. Further, the order in which some or all of theprocess blocks appear in each process should not be deemed limiting.Rather, it should be understood that some of the process blocks can beexecuted in a variety of orders, not all of which can be explicitlyillustrated herein.

With reference to FIG. 10, an example environment 1000 for implementingvarious aspects of the claimed subject matter includes a computer 1002.The computer 1002 includes a processing unit 1004, a system memory 1006,a codec 1035, and a system bus 1008. The system bus 1008 couples systemcomponents including, but not limited to, the system memory 1006 to theprocessing unit 1004. The processing unit 1004 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1006 includes volatile memory 1010 and non-volatilememory 1012, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 1002, such as during start-up, is stored innon-volatile memory 1012. In addition, according to present innovations,codec 1035 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec1035 is depicted as a separate component, codec 1035 can be containedwithin non-volatile memory 1012. By way of illustration, and notlimitation, non-volatile memory 1012 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 1012 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 1012 can be computer memory (e.g., physically integrated withcomputer 1002 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 1010 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 1002 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 10 illustrates, forexample, disk storage 1014. Disk storage 1014 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 1014 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1014 tothe system bus 1008, a removable or non-removable interface is typicallyused, such as interface 1016. It is appreciated that storage devices1014 can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 1036) of the types of information that are stored todisk storage 1014 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 1028).

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1000. Such software includes anoperating system 1018. Operating system 1018, which can be stored ondisk storage 1014, acts to control and allocate resources of thecomputer system 1002. Applications 1020 take advantage of the managementof resources by operating system 1018 through program modules 1024, andprogram data 1026, such as the boot/shutdown transaction table and thelike, stored either in system memory 1006 or on disk storage 1014. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1002 throughinput device(s) 1028. Input devices 1028 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1004through the system bus 1008 via interface port(s) 1030. Interfaceport(s) 1030 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1036 usesome of the same type of ports as input device(s) 1028. Thus, forexample, a USB port can be used to provide input to computer 1002 and tooutput information from computer 1002 to an output device 1036. Outputadapter 1034 is provided to illustrate that there are some outputdevices 1036 like monitors, speakers, and printers, among other outputdevices 1036, which require special adapters. The output adapters 1034include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1036and the system bus 1008. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 1038.

Computer 1002 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1038. The remote computer(s) 1038 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1002. For purposes of brevity, only a memory storage device 1040 isillustrated with remote computer(s) 1038. Remote computer(s) 1038 islogically connected to computer 1002 through a network interface 1042and then connected via communication connection(s) 1044. Networkinterface 1042 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1044 refers to the hardware/softwareemployed to connect the network interface 1042 to the bus 1008. Whilecommunication connection 1044 is shown for illustrative clarity insidecomputer 1002, it can also be external to computer 1002. Thehardware/software necessary for connection to the network interface 1042includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer program product for scenario planning,the computer program product comprising a tangible computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processor to cause the processorto: receive real-time electronic data from a web-crawling system,wherein the real-time electronic data comprises one or moreobservations; based on the one or more observations, iterativelygenerate one or more projected future scenarios for solving a riskmanagement problem, wherein the iterative generation comprises:analyzing real-time content using a topic model, wherein the real-timecontent is associated with at least one defined source and is related toone or more current events; determining that one or more portions of theanalyzed real-time content that are relevant to one or more key riskdrivers of a business entity using a risk driver model; aggregating andtranslating the determined one or more portions into one or moreemerging storylines based on values of one or more attributes of thetopic model; receiving expert domain information regarding at least oneof the one or more key risk drivers, generating the one or moreprojected future scenarios for an organization using at least one of theone or more key risk drivers selected by artificial intelligenceprocesses, based on the one or more emerging storylines, wherein thegeneration of the one or more projected future scenarios comprises:generating an artificial intelligence planning problem based on the atleast one of one or more key risk drivers and the one or more emergingstorylines, wherein the artificial intelligence planning problem isdefined by a tuple comprising a finite set of fluent conditions, a setof actions with preconditions, an initial state of the finite set offluent conditions, and a goal state of the finite set of fluentconditions, generating a plurality of plans based on the artificialintelligence planning problem, and generating the one or more projectedfuture scenarios based upon clustering plans of the plurality of plansaccording to a defined clustering process; and processing the expertdomain information associated with electronic real-time data toiteratively refine artificial intelligence processes, and the one ormore projected future scenarios; and electronically output in real-time,to a computer monitor, electronic visual information via a graphicaluser interface that presents the generated one or more projected futurescenarios in real time in conjunction with the one or more currentevents, wherein the one or more projected future scenarios comprise afuture scenario with one or more leading indicators of the one or morekey risk drivers that cause the future scenario, an emerging risk to thebusiness entity resulting from the future scenario, and an implicationto the business entity of the emerging risk, wherein computation timefor the processor is improved such that the computation time to performactions of the data aggregation component and the artificialintelligence component for the processor is reduced below a definedthreshold.
 2. The computer program product of claim 1, wherein thereal-time electronic data is retrieved by the web-crawling system fromsources comprising news sources, user postings on social media, andtrending topics on social media.
 3. The computer program product ofclaim 1, wherein the topic model comprises key people or organizationsidentified in the expert domain information that are able to influenceat least one key risk driver identified in a risk driver model.
 4. Thecomputer program product of claim 3, wherein the risk driver modelcomprises one or more content monitoring topics corresponding to aleading indicator of a key risk driver, and wherein relevant key riskdrivers for the real-time electronic data are determined based on topicsthat are associated with the one or more content monitoring topics byapplying the topic model.
 5. The computer program product of claim 1,wherein generating the artificial intelligence planning problemcomprises: combining the one or more emerging storylines with one ormore attributes extracted from the real-time electronic data.
 6. Thecomputer program product of claim 3, wherein the risk driver modelcomprises emerging risk information comprising a probable emerging risk,and a possible emerging risk.
 7. A system, comprising: a memory thatstores computer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: a data aggregation componentthat performs one or more methodologies to compute one or more solutionsto a risk management problem and output one or more projected futurescenarios based on electronic data obtained from one or moreweb-crawling systems; an artificial intelligence component that:iteratively generates one or more projected future scenarios, whereinthe iterative generation comprises: analysis of real-time content usinga topic model, wherein the real-time content is associated with at leastone defined source and is related to one or more current events;determination of one or more portions of the analyzed real-time contentthat are relevant to one or more key risk drivers of a business entityusing a risk driver model; aggregation of and translation of thedetermined one or more portions into one or more emerging storylinesbased on values of one or more attributes of the topic model; receipt ofexpert domain information regarding at least one of the one or more keyrisk drivers, generation of the one or more projected future scenariosfor an organization using the at least one of the one or more key riskdrivers selected by artificial intelligence processes based on the oneor more emerging storylines, wherein the generation of the one or moreprojected future scenarios comprises:  generation of an artificialintelligence planning problem based on the at least one of one or morekey risk drivers and the one or more emerging storylines, wherein theartificial intelligence planning problem is defined by a tuplecomprising a finite set of fluent conditions, a set of actions withpreconditions, an initial state of the finite set of fluent conditions,and a goal state of the finite set of fluent conditions,  generation aplurality of plans based on the artificial intelligence planningproblem, and  generation of the one or more projected future scenariosbased upon clustering plans of the plurality of plans according to adefined clustering process; and processing of the expert domaininformation associated with electronic real-time data to iterativelyrefine, artificial intelligence processes, the one or more projectedfuture scenarios; and electronically outputs, to a display device ,electronic visual information via a graphical user interface thatpresents the generated one or more projected future scenarios in realtime in conjunction with the one or more current events, wherein the oneor more projected future scenarios comprise a future scenario with oneor more leading indicators of the one or more key risk drivers thatcause the future scenario, an emerging risk to the business entityresulting from the future scenario, and an implication to the businessentity of the emerging risk, wherein computation time for the processoris improved such that the computation time to perform actions of thedata aggregation component and the artificial intelligence component forthe processor is reduced below a defined threshold.
 8. The system ofclaim 7, wherein the defined source is also selected from the groupconsisting of news sources, user postings on social media, and trendingtopics on social media.
 9. The system of claim 7, wherein the topicmodel comprises key people or organizations that are able to influenceat least one key risk driver identified in the risk driver model. 10.The system of claim 7, wherein the risk driver model comprises one ormore content monitoring topics corresponding to a leading indicator of akey risk driver.
 11. The system of claim 7, wherein the artificialintelligence component aggregates the determined one or more portionsbased on combining the one or more emerging storylines with one or moreattributes extracted from the real-time content.
 12. The system of claim7, wherein the one or more projected future scenarios comprise ascenario from the group consisting of a probable emerging risk, apossible emerging risk, and an associated business implication.